arXiv AI July 21, 2026 By Wei Chen, Yunan Li, Yuan Tian

CarbonNet: How Computer Vision Plays a Role in Climate Change? Application: Learning Geomechanics from Subsurface Geometry of CCS to Mitigate Global Warming

arXiv:2403. 06025v4 Announce Type: replace-cross Abstract: We introduce a new approach using computer vision to predict the land surface displacement from subsurface geometry images for Carbon Capture and Sequestration (CCS).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

Hugging Face Trending Papers

Jun 23

Flood Mapping from RGB imagery using a Vision Foundation Model

Timely, high-resolution maps of flood extent around settlements are essential for emergency response and damage assessment. We consider airborne RGB imagery for flood mapping as it can be collected rapidly at low cost.

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arXiv Machine Learning

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Performance-Carbon Trade-Offs across Architectural Biases in Shear Flow Forecasting

arXiv:2509. 24517v3 Announce Type: replace Abstract: Development of modern deep learning methods has been driven primarily by the push for improving model efficacy (accuracy metrics), leading to large-scale models that require massive computational resources and result in considerable carbon footprint across the model lifecycle.

By Sophia N. Wilson, Jens Hesselbjerg Christensen, Raghavendra Selvan

arXiv AI

Jun 11

AI4Land: Scalable Deep Learning for Global High-Resolution Land Use Reconstruction

arXiv:2606. 11793v1 Announce Type: cross Abstract: Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections, partly driven by the uncertainties affecting the land surface representation and variability in Earth system models.

By Amirpasha Mozaffari, Marina Casta\~no, Stefano Materia, Etienne Tourigny, Oscar Molina-Sedano, Jordi Varela-Agrelo, Dario Garcia-Gasulla, Miguel Castrillo Melguizo, Mario Acosta, Amanda Duarte

arXiv AI

Jun 12

Scalable Deep Learning Framework for Global High-Resolution Land Use Reconstruction

arXiv:2606. 11793v2 Announce Type: replace-cross Abstract: Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections, partly driven by the uncertainties affecting the land surface representation and variability in Earth system models.

By Amirpasha Mozaffari, Marina Casta\~no, Stefano Materia, Etienne Tourigny, Oscar Molina-Sedano, Jordi Varela-Agrelo, Dario Garcia-Gasulla, Miguel Castrillo Melguizo, Mario Acosta, Amanda Duarte

arXiv AI

Jun 15

Clay-CNN Hybrids: Leveraging Geo-Foundational Models as Auxiliary Context for Landslide Detection

arXiv:2606. 14081v1 Announce Type: cross Abstract: Rapid post-event landslide mapping is essential for disaster response but remains difficult to automate due to extreme class imbalance.

By Huong Binh Vu

computer-vision fine-tuning benchmarks

arXiv AI

Jun 16

Clay-CNN Hybrids: Leveraging Geospatial Foundation Models as Auxiliary Context for Landslide Detection

arXiv:2606. 14081v2 Announce Type: replace-cross Abstract: Rapid post-event landslide mapping is essential for disaster response but remains difficult to automate due to extreme class imbalance.

By Huong Binh Vu

computer-vision fine-tuning benchmarks

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Flood Mapping from RGB imagery using a Vision Foundation Model

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Scalable Deep Learning Framework for Global High-Resolution Land Use Reconstruction

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Clay-CNN Hybrids: Leveraging Geospatial Foundation Models as Auxiliary Context for Landslide Detection